Deep Learning for Time Series Cookbook
Use PyTorch and Python recipes for forecasting, classification, and anomaly detection
Created by Cerqueira, Luís Roque
Unlock the power of deep learning for time series data using practical coding recipes in Python and PyTorch. Learn to build accurate models for forecasting, classification, and anomaly detection by applying proven techniques to real-world problems.
Packt | Mar 2024 | 274 min
What You Will Learn
You will start by exploring the fundamentals of time series analysis and deep learning concepts. Step by step, you'll use hands-on coding recipes to tackle real forecasting, classification, and anomaly detection problems. By working directly with PyTorch and Python, you'll gain practical experience building and refining models for production use.
Key Features
- Build forecasting models with PyTorch to predict future trends from time series data
- Apply deep neural networks for time series classification and anomaly detection tasks
- Transform and prepare complex time series data for advanced deep learning architectures
Target Audience
Ideal for machine learning practitioners, data scientists, and analysts with a solid grasp of Python and basic machine learning concepts. If you want to advance your skills in deep learning and apply them to time series forecasting, classification, or anomaly detection, you'll find actionable guidance and practical solutions here.
